Error statistics in data assimilation - PowerPoint Presentation

Slide1

Error statistics in data assimilation

Ross BannisterNCEOUniversity of Reading, UK, r.n.bannister@reading.ac.uk

“All models are wrong …” (George Box)“All models are wrong and all observations are inaccurate” (a data assimilator)Slide2

Distinction between ‘errors’ and ‘error statistics’

When people say ‘errors’ they sometimes (but not always) mean ‘error statistics’Error: The difference between some estimated/measured quantity and its true value.

E.g. εest = xest – x

true

or

εy = y – ytrue Errors are unknown and unknowable quantitiesError Statistics: Some useful measure of the possible values that ε could have. E.g. a PDFError statistics are knowable, although often difficult to determine – even in the Gaussian case.Here, error statistics = second moment (ie assume PDFs are Gaussian and unbiased, <ε> = 0).

ε

PDF(

ε

)

E.g. second moment of

ε

, <ε2> (called a variance), or <ε2>1/2 = σ (standard deviation). If only the variance is known, then the PDF is approximated as a Gaussian. P(ε) ~ exp – ε2/2<ε2>

σ

=

√

<

ε2>

εSlide3

This talk ...

A. What quantities should be assigned error statistics in data assimilation?B. How are error statistics important in data assimilation

?C. ‘Observation’ and ‘state’ vectors.D. ‘Inner’ and ‘outer’ products.

E.

Forms of (Gaussian) error

covariances.F. Link between Bayes’ Theorem and the variational cost function.G. Link between the variational cost function and the ‘BLUE’ formula.H. Example with a single observation.I. Forecast error covariance statistics.A B C D E F G H ISlide4

A. What quantities should be assigned error statistics in data assimilation?

All data that are being fitted to.

Observations (real-world data).

Prior data for the system’s state.

Prior data for any unknown parameters.

Data that have been fitted.Data assimilation-fitted data (analysis, ie posteriori error statistics).}Information available about the system before observations are considered.

A B C D E F G H ISlide5

B. How are error statistics important in data assimilation?

1. Error statistics give a measure of confidence in data

No

assim

Assim

with large obs errors Assim with small obs errors

A B C D E F G H ISlide6

B. How are error statistics important in data assimilation?

2. Error statistics of prior data imply relationships between variables

x

1

x

2

time

Background forecast (no

assim

)

Analysis forecast (consistent with prior and ob errors)

x

1

and x

2

cannot be varied independently by the assimilation here because of the shape of the prior joint PDF.

Known relationships between variables are often exploited to gain knowledge of the complicated nature of the prior error statistics (e.g. changes in pressure are associated with changes in wind in the mid-latitude atmosphere (geostrophic balance).

A B C D E F G H ISlide7

C. ‘Observation’ and ‘state’ vectors

The structure

of the state vector for the example of meteorological fields (u, v, θ, p, q are meteorological 3-D fields; λ, φ

and ℓ are longitude, latitude and

vertical level

). There are n elements in total.x =The observation vector – comprising each observation made. There are p observations.y =

A B C D E F G H ISlide8

D. ‘Inner’ and ‘outer’ products

The inner product (‘scalar’ product) between two vectors gives a scalar

The outer product between two vectors gives a matrix

When

ε is a vector of errors, <

εεT> is a (symmetric) error covariance matrix

A B C D E F G H ISlide9

E. Forms of (Gaussian) error covariances

The one-variable case

The many variable case

0

σ

=

√

<

ε

2

>

<

x

>

A B C D E F G H ISlide10

F. Link between

Bayes’ Theorem and the variational cost function

Bayes theorem links the followingPDF of the observations (given the truth)PDF of the prior information (the background state)

PDF of the state (given the observations – this is the objective of data assimilation)

A B C D E F G H ISlide11

G. Link between the variational cost function and the ‘BLUE’ formula

A B C D E F G H ISlide12

H. Example with a single observation

Analysis increment of the assimilation of a direct observation of one variable.

Obs

of atmospheric pressure →

A B C D E F G H ISlide13

I. Forecast error covariance statistics

In data assimilation prior information often comes from a forecast.

Forecast error covariance statistics (Pf) specify how the forecast might be in error

ε

f = xf – xtrue, Pf = <εf εfT>.How could Pf be estimated for use in data assimilation?Analysis of innovations (*).

Differences of varying length forecasts.

Monte-Carlo method (*).

Forecast time lags.Problems with the above methods.

A climatological average forecast error covariance matrix is called

B.A B C D E F G H ISlide14

I.1 Analysis of innovations

We don’t know the truth, but we do have observations of the truth with known error statistics.Definition of observation error : y =

ytrue + εy = h(xtrue

) +

ε

yDefinition of forecast error : xtrue = xf – εfEliminate xtrue : y = h(xf – εf) + εy ≈ h(xf ) - Hε

f + ε

y‘Innovation’ : y - h(

xf ) ≈ εy - H

εf LHS (known), RHS(unknown)Take pairs of in-situ

obs whose errors are uncorrelated (for variable v1, posn r and v2, r+Δr) y(v1,r) - xf (v1,r) ≈ εy(v1,r) - εf(v1,r) y(v2,r +Δr) - xf (v2,r +Δr) ≈ εy

(v2,r +Δr) -

εf(v2,r +Δr)

Covariances<[y(v1,r) - xf (v1,r)] [y(v2,r +Δr) - xf (v2,r +Δr)]> = <[εy(v1,r) - ε

f(v1,r)] [εy(v

2,r +Δr) - εf(v

2,r +Δr)]> = <εy(v1,r) εy(v2,r +Δr)>

- <εy

(v1,r) ε

f(v2,r +Δr)> - <ε

f

(v

1

,r )

ε

y

(v

2

,r +

Δ

r)>

+ <

ε

f

(v

1

,r)

ε

f

(v

2

,r +

Δ

r)>

↑

↑ ↑

↑

Obs

error covariance

Zero (

obs

and forecast errors

Forecast error covariance

between (v

1

, r) and (v

2

, r+

Δ

r)

uncorrelated)

between (v

1

, r) and (v

2

, r+

Δ

r)

zero unless v1=v

2

and

Δ

r=0

(

one particular matrix element of Pf or B

)

<> average over available observations and sample population of forecasts

A B C D E F G H ISlide15

I.2 Monte-Carlo method (ensembles)

N members of an ensemble of analyses.

Leads to N members of an ensemble of forecasts.The ensemble must capture the errors contributing to forecast errors.

Initial condition errors (forecast/observation/assimilation errors from previous data assimilation).

Model formulation errors (finite resolution, unknown parameters, …).

Unknown forcing.Can be used to estimate the forecast error covariance matrix, e.g.Pf ≈ < (x-<x>) (x-<x>) T > = 1/(N-1) ∑i=1,N (xi - <x>) (xi - <x>)T

Problem: for some applications

N

<< n.n elements of the state vector (in Meteorology can be 10

7).N ensemble members (typically 102).

Consequence – when Pf acts on a vector, the result is forced to lie in the sub-space spanned by the N ensemble members.Ensemblest

x

A B C D E F G H ISlide16

Comments and Questions

!?